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Will Unreasonable CMS Expectations Worsen Knee Osteoarthritis Care?: Commentary on an article by Leo Zalikha, MD, et al.: "Substantial Clinical Benefit After Total Knee Arthroplasty Has Been Set Too High. An Analysis of the American Joint Replacement Registry".

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Can Artificial Intelligence Models Appropriately Recommend Knee Arthroplasty Surgeons?

Colin M Emrich1, Ethan C Gazan1, Alexander J Baur1,2

  • 1Liberty University College of Osteopathic Medicine, Lynchburg, VA, USA.

HSS Journal : the Musculoskeletal Journal of Hospital for Special Surgery
|January 26, 2026
PubMed
Summary
This summary is machine-generated.

Large language models (LLMs) can suggest total knee arthroplasty (TKA) surgeons, but accuracy varies. While 66% of recommendations were appropriate, contact information reliability was low, highlighting the need for further research.

Keywords:
artificial intelligenceinformation seeking behaviororthopedic surgeonpatient participationtotal knee arthroplasty

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Medical Informatics
  • Surgical Recommendation Systems

Background:

  • Large language models (LLMs) are increasingly used by the public for health information.
  • Patients seek AI-driven assistance for medical queries, including surgical referrals.

Purpose of the Study:

  • To evaluate the capability of current LLMs in recommending total knee arthroplasty (TKA) surgeons.
  • To assess the appropriateness and reliability of LLM-generated surgeon recommendations.

Main Methods:

  • Three LLMs (ChatGPT, Gemini, DeepSeek) were prompted to identify TKA surgeons in four US cities.
  • Searches assessed surgeon appropriateness (TKA practice, active status) and contact detail accuracy.
  • LLM rationales and information sources were analyzed.

Main Results:

  • Overall, 66% of LLM recommendations for TKA surgeons were appropriate.
  • Gemini (87%) and ChatGPT (74%) performed better than DeepSeek (36%).
  • Only 23% of provided contact details were accurate, with significant model variation.

Conclusions:

  • LLMs demonstrate potential for identifying TKA surgeons but exhibit variability in accuracy.
  • Reliability of surgeon expertise validation and contact information requires improvement.
  • Further research is needed to understand LLM recommendation criteria for surgical referrals.